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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Quantum Efficiency Gain in 2D Perovskite Photo and X‐Ray Detectors

Abstract The perovskite polycrystalline thin film detector fabricated by solution method is a promising low‐cost, scalable technology for radiation imaging, but its thin volume limits the sensing efficiency for high‐energy X‐ray photons. This work reports 2D perovskite thin film photo‐diodes with a detection gain when sensing visible and X‐ray photons. Detailed power and temperature‐dependent device characterizations reveal a charge multiplication effect to be responsible for the observed high gain. This is caused by a disparity in electron and hole transport, where electron transport is retarded by a trap/de‐trap process via shallow trap states, whereas the hole transport is fast enough to produce a photoconductive gain in satisfying the charge neutrality. The 2D perovskite made with butylamine spacers is also found to exhibit a larger efficiency gain than those made with phenylethylamines because of the higher likelihood of forming shallow traps in the former. The thin film diodes feature a high temporal response over 1 MHz due to the fast charge collection across a thin volume, and the discovery provides device physics mechanisms in connection to material structure‐function relationship for future optoelectronics design that can boost the efficiency of X‐ray sensing and dim light detection.

2D perovskites↗
DOE OSTI2021

Toward the Development of a High-Voltage Mg Cathode Using a Chromium Sulfide Host

Development of Mg-ion batteries as advanced electrochemical energy storage systems relies on the design and discovery of high-voltage positive electrode (cathode) materials. To date, a variety of sulfide cathodes have been reported (e.g., Mg x Mo 6 S 8 , Mg x Ti 2 S4, etc.), but the voltages of these materials are too low to prepare a high energy density Mg cell. Theoretical computations predicted that Mg x Cr 2 S 4 operating with the high-voltage Cr 3+/4+ redox couple would serve as a suitable cathode candidate, but experimental attempts to extract Mg 2+ from the lattice have been largely unsuccessful. Here, we show that reversible electrochemical activity within a thiospinel framework (AB 2 S 4 ) relies on a redox-active transition metal present in the B site; otherwise, anionic redox activity triggers decomposition of the spinel structure. Since Cr and S states are highly coupled in MgCr 2 S 4 , the Cr 3+/4+ redox couple is inaccessible so that reversible (de)intercalation of Mg 2+ cannot occur and charging leads to dissolution of the active material. These findings point to an insufficiency in the screening criteria previously used to identify MgCr 2 S 4 as a promising Mg cathode. Thus, a computable descriptor based on the electronic structure of the discharged material is proposed to predict the prevalence of cation vs anion redox and improve future surveys of potential candidates. It is unlikely that the high-voltage Cr redox couple will be accessible to oxidation in the presence of sulfur within the restrictions of a spinel framework; however, it is possible that a suitable layered Mg x CrS 2 structure could serve as a reversible high-voltage Mg cathode.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Majorana zero modes in impurity-assisted vortex of LiFeAs superconductor

The iron-based superconductor is emerging as a promising platform for Majorana zero mode, which can be used to implement topological quantum computation. One of the most significant advances of this platform is the appearance of large vortex level spacing that strongly protects Majorana zero mode from other low-lying quasiparticles. Despite the advantages in the context of physics research, the inhomogeneity of various aspects hampers the practical construction of topological qubits in the compounds studied so far. Here we show that the stoichiometric superconductor LiFeAs is a good candidate to overcome this obstacle. By using scanning tunneling microscopy, we discover that the Majorana zero modes, which are absent on the natural clean surface, can appear in vortices influenced by native impurities. Our detailed analysis reveals a new mechanism for the emergence of those Majorana zero modes, i.e. native tuning of bulk Dirac fermions. The discovery of Majorana zero modes in this homogeneous material, with a promise of tunability, offers an ideal material platform for manipulating and braiding Majorana zero modes, pushing one step forward towards topological quantum computation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of growth substrate on the nucleation of monolayer MoTe 2

The discovery and characterization of two-dimensional (2D) materials beyond graphene has increased dramatically over the past decade with increasingly fine control over the growth dynamics of these materials. MoTe 2 represents a model material for studying phase change properties of 2D materials due to small energy differences between its 2H semiconducting phase and 1T' semimetallic phase. Although some substrates are demonstrated to be better than others for the wafer-scale growth of high quality MoTe 2 films, substrate effects on the nucleation and growth of MoTe 2 are still not well understood. Here, we grow monolayer MoTe 2 by converting MoO x thin films deposited on three different substrates: sapphire Al 2 O 3 (0001), amorphous SiO 2 , and amorphous AlO x , and examine the early stages of the conversion reaction to elucidate the substrate effects on the nucleation of MoTe 2 . We observe that the chemical composition of the substrate is more important than the surface topography and crystallinity of the substrate, with high quality monolayer 2H MoTe 2 formed on both Al 2 O 3 (0001) and AlO x in contrast to mixed phase 2H/1T' MoTe 2 formed on SiO 2 , as determined by Raman spectroscopy, X-ray photoelectron spectroscopy, and atomic force microscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electronic properties of correlated kagomé metals AV 3 Sb 5 (A = K, Rb, and Cs): A perspective

Following the discovery of a new family of kagomé prototypical materials with structure AV 3 Sb 5 (A=K, Rb, and Cs), there has been a heightened interest in studying the correlation-driven electronic phenomena in these kagomé lattice systems. The study of these materials has gone beyond magneto-transport measurements to reveal exciting features such as Dirac bands, anomalous Hall effect, bulk superconductivity with T c ~0.9–2.5K, and the observation of charge density wave instabilities, suggesting an intertwining of topological physics and new quantum orders. Moreover, very recent works on numerous types of experiments have appeared further examining the unconventional superconductivity and the exotic electronic states found within these kagomé materials. Theories on the strong interactions that play a role in these systems have been proposed to shed light on the nature of these topological charge density waves. In this brief review, we summarize these recent experimental findings and theoretical proposals and envision the materials as new platforms to study the interplay between topological physics and strongly correlated electronic systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Searching for ternary magnetic Zr–Fe–B compounds through deep machine learning

We use deep machine learning (ML) combined with first-principles calculations to search for energetically favorable ternary magnetic zirconium–iron borides. We show that an iterative ML approach enables efficient screening of vast structural libraries, effectively selecting promising candidates for subsequent first-principles investigations. Twenty-two new Fe-rich ternary compounds with formation energies within 60 meV atom −1 above the known ternary convex hull and with magnetic polarization larger than 0.6 T are identified, among which ten structures exhibit significant uniaxial anisotropy with magnetocrystalline anisotropy constant K 1 ⩾ 0.8 MJ m −3 , including a Zr 2 Fe 14 B phase. Such an ML-guided approach dramatically accelerates the discovery of rare-earth-free permanent magnetic materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Large spin Hall effect in Si at room temperature

Silicon's weak intrinsic spin-orbit coupling and centrosymmetric crystal structure are a critical bottleneck to the development of Si spintronics, because they lead to an insignificant spin Hall effect (spin current generation) and inverse spin Hall effect (spin current detection). In this work, we undertake current, magnetic field, crystallography dependent magnetoresistance, and magnetothermal transport measurements to study the spin transport behavior in freestanding Si thin films. We observe a large spin Hall magnetoresistance in both $\mathcal{p}$ – Si and $\mathcal{n}$ – Si at room temperature and it is an order of magnitude larger than that of Pt. One explanation of the unexpectedly large and efficient spin Hall effect is spin-phonon coupling instead of spin-orbit coupling. The macroscopic origin of the spin-phonon coupling can be large strain gradients that can exist in the freestanding Si films. This discovery in a light, earth abundant and centrosymmetric material opens a new path of strain engineering to achieve spin dependent properties in technologically highly developed materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Superconductivity in the bilayer Hubbard model: Two Fermi surfaces are better than one

Fully occupied or unoccupied bands in a solid are often considered inert and irrelevant to a material's low-energy properties. But the discovery of enhanced superconductivity in heavily electron-doped FeSe-derived superconductors poses questions about the possible role of incipient bands (those laying close to but not crossing the Fermi level) in pairing. To answer this question, researchers have studied pairing correlations in the bilayer Hubbard model, which has an incipient band for large interlayer hopping t⊥, using many-body perturbation theory and variational methods. They have generally found that superconductivity is enhanced as one of the bands approaches the Lifshitz transition and even when it becomes incipient. Here we address this question using the nonperturbative quantum Monte Carlo (QMC) dynamical cluster approximation (DCA) to study the bilayer Hubbard model's pairing correlations. We find that the model has robust s± pairing correlations in the large t⊥ limit, which can become stronger as one band is made incipient. While this behavior is linked to changes in the effective interaction, we further find that it is counteracted by a suppression of the intrinsic pair-field susceptibility and does not translate to an increased Tc. Our results demonstrate that the highest achievable transition temperatures in the bilayer Hubbard model occur when the system has two bands crossing the Fermi level.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Monolayer 2D semiconducting tellurides for high-mobility electronics

Discovery and design of two-dimensional (2D) materials with suitable band gaps and high carrier mobility are of vital importance for the photonics, optoelectronics, and high-speed electronics. In this work, based on first principles calculations using density functional theory with Perdew-Burke-Ernzerhof and Heyd-Scuseria-Ernzerhoffunctionals, we introduce a family of monolayer isostructural semiconducting tellurides MNTe 4 , with M={Ti,Zr,Hf} and N={Si,Ge}. We find that these compounds have been identified to possess direct band gaps from 1.0 to 1.31 eV, which are well suited for photonics and optoelectronics applications. Additionally, anisotropic in-plane transport behavior is observed, and small electron and hole (0.11–0.15m e ) effective masses are identified along the dominant transport direction. Ultrahigh carrier mobility is predicted for this family of 2D compounds, which host great promise for potential applications in high-speed electronic devices. Furthermore, detailed analysis of electronic structures reveals the origins of the promising properties of this unique class of 2D telluride materials.

2-dimensional systems↗
DOE OSTI2024

Size-dependent ferroelectric-to-paraelectric sliding transformations and antipolar-to-ferroelectric topological phase transitions in binary homobilayers

The recent discovery of ferroelectric behavior in few-layer materials, accompanied by the observation of antipolar domains in hexagonal boron nitride and transition metal dichalcogenide moiré bilayers, is paving the way for revolutionary advancements in the generation and manipulation of intrinsic electric dipoles through stacking. In addition, these cutting-edge quantum materials are reshaping our comprehension of phase transitions. Within the present paper, we unveil a size-dependent sliding behavior that marks a significant departure from conventional ferroelectrics. We also shed light on thermally induced spontaneous hyperlubric sliding within moiré bilayers, which can be used as a signal to distinguish topological phase transitions from an antipolar onto a ferroelectric bilayer. Furthermore, our findings also suggest that the (topological) pinning of AA nodes in antipolar moiré homobilayers prevents the occurrence of an antipolar-to-paraelectric transformation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

When Machine Learning Meets 2D Materials: A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

2D materials↗
DOE OSTI2023

Spiroborate-Linked Ionic Covalent Adaptable Networks with Rapid Reprocessability and Closed-Loop Recyclability

Covalent adaptable networks (CANs) represent a novel class of polymeric materials crosslinked by dynamic covalent bonds. Since their first discovery, CANs have attracted great attention due to their high mechanical strength and stability like conventional thermosets under service conditions and easy reprocessability like thermoplastics under certain external stimuli. Here, we report the first example of ionic covalent adaptable networks (ICANs), a type of crosslinked ionomers, consisting of negatively charged backbone structures. More specifically, two ICANs with different backbone compositions were prepared through spiroborate chemistry. Given the dynamic nature of the spiroborate linkages, the resulting ionomer thermosets display rapid reprocessability and closed-loop recyclability under mild conditions. The materials mechanically broken into smaller pieces can be reprocessed into coherent solids at 120 °C within only 1 min with nearly 100% recovery of the mechanical properties. Upon treating the ICANs with dilute hydrochloric acid at room temperature, the valuable monomers can be easily chemically recycled in almost quantitative yield. Furthermore, this work demonstrates the great potential of spiroborate bonds as a novel dynamic ionic linkage for development of new reprocessable and recyclable ionomer thermosets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A Reflection on the Evolution of Sodium/Metal Chloride (ZEBRA) Batteries and Lithium-Ion Cathode Materials (1975–2025): A Tribute to Johan Coetzer

This narrative summarizes, in retrospect, the enormous and quietly-conducted contribution of Dr Johan Coetzer, a South African scientist and entrepreneur, to the discovery, development and implementation of the high temperature sodium—metal chloride “ZEBRA” battery (Na/β-Al 2 O 3 , NaAlCl 4 /MCl 2 (M = Ni, Fe)). Here, this research activity was initiated in the mid 1970’s at the Council for Scientific and Industrial Research (CSIR) in South Africa and subsequently developed primarily in partnership with the Atomic Energy Research Establishment (AERE, Harwell, UK), Beta R&D (Derby, UK) and Daimler Benz (Germany), before being transferred to industry worldwide for electric vehicle and energy storage applications, albeit at a relatively low production rate. Coetzer’s wide-ranging, innovative and, at times, unconventional scientific approach also laid the foundation for the discovery and implementation of manganese-based spinel and layered metal oxide cathode materials for the Li-ion battery industry.

ZEBRA Battery↗
DOE OSTI2021

Quasi-One-Dimensional Structure and Possible Helical Antiferromagnetism of RbMn 6 Bi 5

Quasi-one-dimensional materials exhibit not only unique crystal structure but also abundant physical properties such as charge density wave, Luttinger liquid, and superconductivity. In this study, we report the discovery, structure, and physical properties of a new manganese-based quasi-one-dimensional material RbMn 6 Bi 5 , which crystallizes in a monoclinic space group C 2/ m (No. 12) with lattice parameters a = 23.286(5) Å, b = 4.6215(9) Å, c = 13.631(3) Å, and β = 125.00(3)°. The structure features [Mn 6 Bi 5 ] –1 double-walled column extending along the [010] direction, together with Bi–Bi homoatomic bonds linking the columns and the countercation Rb + . The temperature-dependent resistivity clearly indicates a significant resistivity anisotropy for RbMn 6 Bi 5 , whereas the magnetic susceptibility and specific heat measurements show that RbMn 6 Bi 5 is antiferromagnetic below 82 K. The density functional theory calculations indicate that RbMn 6 Bi 5 is a quasi-one-dimensional metal with possible helical antiferromagnetic configuration. The discovery of RbMn 6 Bi 5 confirms the viability of discovering new quasi-one-dimensional materials in manganese-based compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning

Abstract While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, extracting fungible knowledge representations from experimental data remains an elusive task. In this manuscript, we use ML to infer the underlying differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). Using a sparse regression algorithm, we find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85 °C is described minimally by a second-order polynomial. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous to self-propagating reactions. We examine the robustness of our conclusions to experimental variance and Gaussian noise and describe the experimental limits within which this methodology can be applied. Our study highlights the promise and challenges associated with ML-aided scientific discovery by demonstrating its application in experimental chemical and materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Integrating Experiments, Simulations, and Artificial Intelligence to Accelerate the Discovery of High-Performance Green Composites

The imperative for incorporating greener materials into the aerospace industry necessitates addressing significant challenges associated with the microstructural variability exhibited by recycled and sustainable feedstocks. In this study, we propose an integrated methodology that combines experimental investigations, finite element analysis, and artificial intelligence to develop sustainable composites with consistent properties. Our approach utilizes a pipeline comprising an automated mechanical tester, a finite element method simulator, and a convolutional neural network predictor to identify and optimize fabrication parameters for achieving desired mechanical characteristics in composites. By employing a nested-loop pipeline, our methodology improves sample efficiency, accuracy, and effectively bridges the gap between simulations and real-world performance. This unique methodology offers a promising avenue for facilitating the adoption of aerospace-appropriate green composites.

Athanasiou, Christos↗